The Case for Small Data

It is reasonable for management to want evidence of a problem before they put resources into fixing it, and we would like to be evidence driven ourselves. All too often, though, we assume that having good data means having more data. We live in the time of Big data where the value of some companies is measured in terms of the size of the data set they hold. Our phones collect and report our habits, our movements, and our interests, so that processing clusters can predict our future behaviour. It is tempting to think that to understand safety better and do safety better, we need more numbers.

Unless you work as a data analyst, most of us are limited to the use of ‘small data’ – we are limited to observing or listening to individual or unique experiences. What good is that? Can you really ‘speak truth to power’ when your information comes from whimsical stories, from people with their own agendas, biased by desires to tell a compelling story involving themselves? Against 10,000s of data points it may seem like a futile exercise. But individual experiences are data too – and they have a power to engage and inspire change that numbers do not.

Consider what a supervisor once shared:

I had FedEx, the other coordinator had StarTrack. Both had half a crew each. FedEx made deadline by 30 seconds. It was just a mad mad rush, push, sweat, scream just to get the goods out. Severely undermanned. Not enough leading hands, only one leading hand on shift every night.

When we listen to the experiences of the end-users of the many structures, processes, and systems set in place to govern what should happen at work, a different picture emerges. There are, in all likelihood, no procedures instructing people to scream, to sweat and to madly rush work, but sometimes that is what work is like. These stories draw attention to things that are entangled and emergent. It brings out the messy details and other real aspects that impact how work is carried out. Relationships, feelings, mess, smells, perspiration, sounds, screaming, anxiety, pain, joy, pride and camaraderie are all highly unlikely to be captured by big data. Yet, all of these aspects impact work, and safety. Unless you have ways to know about how such aspects are involved in work, you will never know. Engaging with the unique experience is pretty much the only way to get to that. And when you start with this experience, work as done comes to the fore and engages tellers and listeners alike in sensing how things actually come together, rather than what should happen.

And consider this mechanic’s experience:

There are so many tools and equipment problems. One simple thing is a thermometer. Just a normal thermometer, just to measure the local temperature, which is a very small thing but is very important for cable tension. We cannot sign the paperwork, we cannot finish the work if we don’t have the thermometer with us because cable tension varies with the temperature and we have to get that temperature to set up how much tension we will put. They don’t have a thermometer. I’ve been looking and asking around for it for the last five days. Couldn’t find it. I eventually used the iPhone to read the temperature of where we are.

This story does not explain why the thermometer is missing. But it does hint about the impact. The obvious impact of loss of precision from using iPhone information is in there (and the increased risk that comes with that), but also hints about frustration on the part of the person who for five days was looking for the thermometer.

Stories such as the ones above are subversive in the way that they embrace the tension between the plans and the unexpected. After hearing what it’s like to work within an imperfect system, it can make a lot of sense to go about work in a particular, less than optimal, way. Such stories convince not by their objective truth, but by their ‘aesthetics’ or emotional appeal on the listener or observer (surprising, touching, humorous, upsetting). By listening to end users unique experiences, we enter a perspective in which we can see normal humans doing normal work in trying to create success amongst scarcity, imperfect and conflicting setups. Work governance, and safety ambitions, are more likely to turn into a need to support and provide, rather than to constrain and enforce. Can big data do that?

The stories people tell about work often also have an ethical dimension – they tell about what is right and what is wrong, what a good workplace should look like, and what it is like to operate within, or outside, that ethical space. A welder once shared:

One of the days I did do a Take 5 and I was doing hot work and the Take 5 didn’t help me. I ended up getting burnt. I ended up in the hospital. The first thing the corporate guy did was ask if I had a Take 5, and that’s all they wanted to know: if I had a Take 5 done.

Then I said yes, in my shirt which is all the same. Well do you mind if I have a look? Go for your life I don’t give a shit at this point.

But that’s what the standard paper trail is.. so if anything comes down to it.. if you don’t have one it’s got nothing to fall back on them. It’s all you.

Compatible or incompatible values become visible in the clashes or meetings between different mentalities. These meetings or clashes are matters of the heart, and critically important for how we create workplaces and collaborate. I don’t think that big data can ever capture these. I don’t even think that big data at all can capture what is ethical. What is right and wrong, is not something that can be proven. Not in numbers, nor in cause and effect relationships. But these issues are as real as productivity rates or the number of checks performed. In the current strive for large truths about work, discussions about ethics and morale, about what is right and wrong, risk being (further) marginalised.

While numbers can give indications and insights that are valuable, the reliance on a calculative approach to understanding work risk shifting attention away from those things that are real to the people who do the work. To overcome, I see no other option that to emphasise the importance and potential of using descriptions about what goes on at work. Such descriptions do not have cause and effect perfectly outlined that allow precise interventions, and they may be ambiguous and open-ended. But that is the beauty of them. They are as difficult and messy as work often is. And more and more people can be invited to interpret and contribute to increasingly large conversations about work. By engaging with ‘small data’ organisations stand a better chance to get to understand and engage the heart of what happens at work.

Note: Thanks to Ron Gantt and Drew Rae for insightful discussions and contributions to this text!